The Turing test was originally conceived by Alan Turing to determine if a machine had achieved human-level intelligence. Although no longer taken as a comprehensive measure of human intelligence, passing the Turing test remains an interesting challenge as evidenced by the still unclaimed Loebner prize, a high profile prize for the first AI to pass a Turing style test.

In this presentation, based on the paper by Joseph MacInnes, Blair Armstrong, Dwayne Pare, George Cree and Steve Joordens, MacInnes sketches the development of an artificial “Turing judge” capable of critically evaluating the likelihood that a stream of discourse was generated by a human or a computer. The knowledge our judge uses to make the assessment comes from a model of human lexical semantic memory known as latent semantic analysis.

They provide empirical evidence that our implemented judge is capable of distinguishing between human and computer generated language from the Loebner Turing test competition with a degree of success similar to human judges.